Digital therapeutic systems and methods

ABSTRACT

Methods and devices include identifying a plurality of target users for the digital therapeutic based on one or more target parameters, conducting outreach to one or more of the plurality of target users using an outreach medium, identifying an activation mechanism to optimize use of the digital therapeutic, and encouraging an engagement level of the digital therapeutic by one or more of the plurality of target users.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims the benefit of priorityto U.S. patent application Ser. No. 17/177,695, filed on Feb. 17, 2021,which is a continuation of International Application No.PCT/US2020/063474, filed Dec. 4, 2020, which claims the benefit of U.S.Provisional Application No. 62/943,536, filed Dec. 4, 2019, theentireties of each of which are incorporated by reference herein.

TECHNICAL FIELD

The present disclosure relates generally to obtaining and processingdata to implement a digital therapeutic system adapted to improve thehealth of a user.

INTRODUCTION

Increased healthcare costs have limited patient access to appropriatecare. At the same time, healthcare companies have increased providerworkloads and limited physician-patient interactions. Digitaltherapeutics can offer a reduction in cost and a novel treatmentimplementation. However, digital therapeutics have yet to achievecritical mass due to a lack of a standardized value chain, lack of keyprocesses, lack of metrics, and lack of best practices and benchmarking.

The present disclosure is directed to addressing one or more of theabove-referenced challenges. The introduction provided herein is for thepurpose of generally presenting the context of the disclosure. Unlessotherwise indicated herein, the materials described in this section arenot prior art to the claims in this application and are not admitted tobe prior art, or suggestions of the prior art, by inclusion in thissection.

SUMMARY

This disclosure is directed to a computer-implemented method fordeploying a digital therapeutic including identifying a plurality oftarget users for the digital therapeutic based on one or more targetparameters, conducting outreach to one or more of the plurality oftarget users using an outreach medium, identifying an activationmechanism to optimize use of the digital therapeutic, and encouraging anengagement level of the digital therapeutic by one or more of theplurality of target users.

Techniques disclosed include generating a report based on one or more ofthe target users, the outreach, the activation mechanism, or theengagement level. The report may be based on one or more of aninformative analysis, discovery analysis, extrapolative analysis, or anadaptive analysis. The report may include a comparison of an N+1 stagescore to an N stage score.

The plurality of target users are identified based on one or more ofclinical factors, disease factors, technology factors, social factors,or demographic factors. The outreach is conducted based on one or moreof a method, a modality, a frequency, a time, or a level of interaction.The activation mechanism is based on one or more of a modality,data-enablement verses data-entry, or a location. The engagement levelis based on one or more of an in-solution versus out-of-solution, afrequency, a length, and a modality. At least one of the identifying theplurality of target users, conducting outreach, identifying anactivation mechanism, and encouraging an engagement level is based on anoutput of a machine learning model. The machine learning model istrained my modifying one of one or more weights or one or more layersbased on training data. The training data comprises one or more of stageinputs, known outcomes, and comparison results. The comparison resultsare a ratio of an N+1 stage score to an N stage score.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate examples of the disclosure andtogether with the description, serve to explain the principles of thedisclosure.

FIG. 1 is a schematic illustration of a health management system,according to an example of the present disclosure.

FIG. 2 is a schematic illustration of a portion of the health managementsystem of FIG. 1.

FIG. 3 is a schematic illustration of another portion of the healthmanagement system of FIG. 1.

FIG. 4 is a flowchart of a digital therapeutic method, according to anexample of the present disclosure.

FIG. 5 is a plurality of stage based flowcharts, according to an exampleof the present disclosure.

FIG. 6 flowchart for generating comparison results, according to anexample of the present disclosure.

FIG. 7 flowchart for training a machine learning model, according to anexample of the present disclosure.

FIG. 8 is a simplified functional block diagram of a computer that maybe configured as a host server, for example, to function as healthcareprovider decision-making server, according to an example of the presentdisclosure.

FIG. 9 is a chart showing experimental results, according to an exampleof the present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to examples of the disclosure,which are illustrated in the accompanying drawings. Wherever possible,the same reference numbers will be used throughout the drawings to referto the same or like parts.

In the discussion that follows, relative terms such as “about,”“substantially,” “approximately,” etc. are used to indicate a possiblevariation of ±10% in a stated numeric value. It should be noted that thedescription set forth herein is merely illustrative in nature and is notintended to limit the examples of the subject matter, or the applicationand uses of such examples. Any implementation described herein asexemplary is not to be construed as preferred or advantageous over otherimplementations. Rather, as alluded to above, the term “exemplary” isused in the sense of example or “illustrative,” rather than “ideal.” Theterms “comprise,” “include,” “have,” “with,” and any variations thereofare used synonymously to denote or describe a non-exclusive inclusion.As such, a process, method, article, or apparatus that uses such termsdoes not include only those steps, structure or elements but may includeother steps, structures or elements not expressly listed or inherent tosuch process, method, article, or apparatus. Further, the terms “first,”“second,” and the like, herein do not denote any order, quantity, orimportance, but rather are used to distinguish one element from another.Moreover, the terms “a” and “an” herein do not denote a limitation ofquantity, but rather denote the presence of at least one of thereferenced item.

Healthcare and Computing Environment

FIG. 1 is a block diagram of a health management system 100, accordingto an example of the present disclosure. A user (e.g., a patient,consumer, or the like) 8 having an electronic device 19, such as amobile device, computer, medical device, or any other electronic deviceconfigured to access an electronic network 32, such as the Internet, maycommunicate with or otherwise access a mobile health (mHealth)application 1. The mHealth application 1 is an example of a digitaltherapeutic, as further disclosed herein. According to some examples,network 32 may include wireless or wired links, such as mobile telephonenetworks, Wi-Fi, LANs, WANs, Bluetooth, near-field communication (NFC),or other suitable forms of network communication. Multiple electronicdevices 19 may be configured to access electronic network 32. A user 8may access mHealth application 1 with a single account linked tomultiple electronic devices 19 (e.g., via one or more of a mobile phone,a tablet, and a laptop computer). Electronic device 19 also may include,but is not limited to, mobile health devices, a desktop computer orworkstation, a laptop computer, a mobile handset, a personal digitalassistant (PDA), a cellular telephone, a network appliance, a camera, asmart phone, a smart watch, an enhanced general packet radio service(EGPRS) mobile phone, a media player, a navigation device, a gameconsole, a set-top box, a biometric sensing device with communicationcapabilities, a smart TV, or any combination of these or other types ofcomputing devices having at least one processor, a local memory, adisplay (e.g., a monitor or touchscreen display), one or more user inputdevices, and a network communication interface. The electronic device 19may include any type or combination of input/output devices, such as adisplay monitor, keyboard, touchpad, accelerometer, gyroscope, mouse,touchscreen, camera, a projector, a touch panel, a pointing device, ascrolling device, a button, a switch, a motion sensor, an audio sensor,a pressure sensor, a thermal sensor, and/or microphone. Electronicdevices 19 also may communicate with each other by any suitable wired orwireless means (e.g., via Wi-Fi, radio frequency (RF), infrared (IR),Bluetooth, Near Field Communication, or any other suitable means) tosend and receive information.

mHealth application 1 may be in communication with other entities ornetworks to send and receive information. In some examples, mHealthapplication 1 may communicate with one or more applications associatedwith the user 8 such as, e.g., exercise tracking (e.g., step tracking)applications and/or other health-related applications. mHealthapplication 1 may be able to import data from the other applications toanalyze and use in generating treatment plans for the user 8. Forexample, mHealth application 1 may import activity tracking data fromanother application and use that data to identify patterns between user8 exercise and blood glucose values collected prior to the use ofmHealth application 1. mHealth application 1 also may import any othersuitable data from other mobile health applications such as, e.g., bloodpressure, BMI, A1C, exercise type, exercise duration, exercise distance,calories burned, total steps, exercise date, exercise start and stoptimes, and sleep. mHealth application 1 also may export data to othermobile applications, including, e.g., other mobile health applicationshaving social or interactive features. A healthcare provider 7, such asa physician, may prescribe the application. However, it is alsocontemplated that mHealth application 1 may not require a prescription,e.g., that it may be a commercially available consumer applicationaccessible without a prescription from a digital distribution platformfor computer software. mHealth application 1 may be tailored to aspecific user 8 and may be activated in person by the user 8 by visitinga pharmacy 9 or other authorized entity. For example, the user 8 mayreceive an access code from the pharmacy that authorizes access tomHealth application 1. The user 8 may receive training on using mHealthapplication 1 by a mHealth support system 25 and/or application trainer24. mHealth application 1 may include programming 28 of various forms,such as machine learning programming algorithms 26. The user treatmentplan may include a prescription (e.g., for a drug, device, and/ortherapy), which may be dispensed by the pharmacy 9. The pharmacy 9 mayallow the refill of the prescribed product/therapy after receivingauthorization based on the user's compliance with his/her healthcaretreatment plan. The authorization may be received by the pharmacy 9 by acommunication from the application 1, via, e.g., the network 32 andvarious servers 29. Use of the drug or other medical product/therapyalso may be sent to the manufacturer 37 over the network 32 to informthe manufacturer 37 of the amount of medical product or therapy beingused by user 8. This information may assist the manufacturer 37 inassessing demand and planning supply of the medical product or therapy.The healthcare provider 7 also may receive a report based on the userinformation received by the application 1, and may update the usertreatment plan based on this information. The user's electronic medicalrecord 14 also may be automatically updated via the network 32 based onthe user information, which may include electronically transmitted user8 feedback on the application, received by mHealth application 1.Healthcare provider 7 may be any suitable healthcare provider including,e.g., a doctor, specialist, nurse, educator, social worker, MA, PA, orthe like.

FIG. 2 is a schematic diagram of additional aspects of system 100. Forexample, the system 100 may access decision models stored on a decisionmodel database 270 via network 32. The retrieved decision models may beused for display and/or processing by one or more electronic devices 19,such as a mobile device 215, a tablet device 220, a computer (e.g., alaptop or desktop) 225, a kiosk 230 (e.g., at a kiosk, pharmacy, clinic,or hospital having medical and/or prescription information), and/or anydevice connected to network 32.

In the example shown in FIG. 2, mobile device 215, tablet 220, andcomputer 225 each may be equipped with or include, for example, a GPSreceiver for obtaining and reporting location information, e.g., GPSdata, via network 32 to and from any of servers 29 and/or one or moreGPS satellites 255.

Each of electronic devices 19, including mobile device 215, tabletdevice 220, computer 225, and/or kiosk 230, may be configured to sendand receive data (e.g., clinical information) to and from a system ofservers 29 over network 32. Each of devices 19 may receive information,such as clinical data via the network 32 from servers 29. Servers 29 mayinclude clinical data servers 240, algorithm servers 245, user interface(UI) servers 250, and/or any other suitable servers. Electronic device19 may include a user interface that is in data communication with UIserver 250 via network 32. Each server may access the decision modeldatabase 270 to retrieve decision models. Each server may includememory, a processor, and/or a database. For example, the clinical dataserver 240 may have a processor configured to retrieve clinical datafrom a provider's database and/or a patient's electronic medical record.The algorithm server 245 may have a database that includes variousalgorithms, and a processor configured to process the clinical data. TheUI server 250 may be configured to receive and process user 8 input,such as clinical decision preferences. The satellite 255 may beconfigured to send and receive information between servers 29 anddevices 19.

The clinical data server 240 may receive clinical data, such as dataregarding the user from the electronic device 19 via the network 32 orindirectly via the UI server 250. The clinical data server 240 may savethe information in memory, such as a computer readable memory.

The clinical data server 240 also may be in communication with one ormore other servers, such as the algorithm server 245 and/or externalservers. The servers 29 may include data about provider preferences,and/or user 8 health history. In addition, the clinical data server 240may include data from other users. The algorithm server 245 may includemachine learning, and/or other suitable algorithms. The algorithm server245 also may be in communication with other external servers and may beupdated as desired. For example, the algorithm server 245 may be updatedwith new algorithms, more powerful programming, and/or more data. Theclinical data server 240 and/or the algorithm server 245 may process theinformation and transmit data to the model database 270 for processing.In one example, algorithm server(s) 245 may be obtain a patterndefinition in a simple format, predict several time steps in the futureby using models, e.g., Markov models, Gaussian, Bayesian, and/orclassification models such as linear discriminant functions, nonlineardiscriminant functions, random forest algorithms and the like, optimizeresults based on its predictions, detect transition between patterns,obtain abstract data and extract information to infer higher levels ofknowledge, combine higher and lower levels of information to understandabout the user 8 and clinical behaviors, infer from multi-temporal(e.g., different time scales) data and associated information, usevariable order Markov models, and/or reduce noise over time by employingclustering algorithms, such as k-means clustering.

Each server in the system of servers 29, including clinical data server240, algorithm server 245, and UI server 250, may represent any ofvarious types of servers including, but not limited to, a web server, anapplication server, a proxy server, a network server, or a server farm.Each server in the system of servers 29 may be implemented using, forexample, any general-purpose computer capable of serving data to othercomputing devices including, but not limited to, devices 19 or any othercomputing device (not shown) via network 32. Such a general-purposecomputer can include, but is not limited to, a server device having aprocessor and memory for executing and storing instructions. The memorymay include any type of random access memory (RAM) or read-only memory(ROM) embodied in a physical storage medium, such as magnetic storageincluding floppy disk, hard disk, or magnetic tape; semiconductorstorage such as solid-state disk (SSD) or flash memory; optical discstorage; or magneto-optical disc storage. Software may include one ormore applications and an operating system. Hardware can include, but isnot limited to, a processor, memory, and graphical UI display. Eachserver also may have multiple processors and multiple shared or separatememory components that are configured to function together within, forexample, a clustered computing environment or server farm.

FIG. 3 is another representation of a portion of system 100 showingadditional details of electronic device 19 and a server 29. Electronicdevice 19 and server 29 each may contain one or more processors, such asprocessors 301-1 and 304-1. Processors 301-1 and 304-1 each may be acentral processing unit, a microprocessor, a general purpose processor,an application specific processor, or any device that executesinstructions. Electronic device 19 and server 29 also may include one ormore memories, such as memories 301-2 and 304-2, that store one or moresoftware modules. Memories 301-2 and 304-2 may be implemented using anycomputer-readable storage medium, such as hard drives, CDs, DVDs, flashmemory, RAM, ROM, etc. Memory 301-2 may store a module 301-3, which maybe executed by processor 301-1. Similarly, memory 304-2 may store amodule 304-3, which may be executed by processor 304-1.

Electronic device 19 may further comprise one or more UIs. The UI mayallow one or more interfaces to present information to a user 8, such asa plan or intervention. The UI may be web based, such as a web page, ora stand-alone application. The UI also may be configured to acceptinformation about a user 8, such as data inputs and user feedback. Theuser 8 may manually enter the information, or it may be enteredautomatically. In an example, the user 8 (or the user's caretaker) mayenter information such as when medication was taken or what food anddrink the user 8 consumed. Electronic device 19 also may include testingequipment (not shown) or an interface for receiving information fromtesting equipment. Testing equipment may include, for example, a bloodglucose meter, heart rate monitor, weight scale, blood pressure cuff, orthe like. The electronic device 19 also may include one or more sensors(not shown), such as a camera, microphone, or accelerometer, forcollecting feedback from a user 8. In one example, the device mayinclude a glucose meter for reading and automatically reporting theuser's blood glucose levels.

Electronic device 19 also may include a presentation layer. Thepresentation layer may be a web browser, application, messaginginterface (e.g., e-mail, instant message, SMS, etc.), etc. Theelectronic device 19 may present notifications, alerts, readingmaterials, references, guides, reminders, or suggestions to a user 8 viapresentation layer. For example, the presentation layer may presentarticles that are determined to be relevant to the user 8, reminders topurchase medications, tutorials on topics (e.g., a tutorial oncarbohydrates), testimonials from others with similar symptoms, and/orone or more goals (e.g., a carbohydrate counting goal). The presentationlayer also may present information such as a tutorial (e.g., a userguide or instructional video) and/or enable communications between thehealthcare provider, and the user 8, e.g., patient. The communicationsbetween the healthcare provider, and the user 8, e.g., patient, may bevia electronic messaging (e.g., e-mail or SMS), voice, or real-timevideo. One or more of these items may be presented based on a treatmentplan or an updated treatment plan, as described later. The presentationlayer also may be used to receive feedback from a user.

The system 100 also may include one or more databases, such as adatabase 302. Database 302 may be implemented using any databasetechnology known to one of ordinary skill in the art, such as relationaldatabase technology or object-oriented database technology. Database 302may store data 302-1. Data 302-1 may include a knowledge base for makinginferences, statistical models, and/or user information. Data 302-1, orportions thereof, may be alternatively or simultaneously stored inserver 29 or electronic device 19.

System 100 can be used for a wide range of applications, including, forexample, addressing a user's healthcare, maintaining a user's finances,and monitoring and tracking a user's nutrition and/or sleep. In someimplementations of system 100, any received data may be stored in thedatabases in an encrypted form to increase security of the data againstunauthorized access and complying with HIPAA privacy, and/or otherlegal, healthcare, financial, or other regulations.

For any server or server systems 29 depicted in system 100, the serveror server system may include one or more databases. In an example,databases may be any type of data store or recording medium that may beused to store any type of data. For example, database 302 may store datareceived by or processed by server 29 including information related to auser's treatment plan, including timings and dosages associated witheach prescribed medication of a treatment plan. Database 302 also maystore information related to the user 8 including their literacy levelrelated to each of a plurality of prescribed medications.

Health Conditions

Diabetes mellitus (commonly referred to as diabetes) may be a chronic,lifelong metabolic disease (or condition) in which a patient's body isunable to produce any or enough insulin, or is unable to use the insulinit does produce (insulin resistance), leading to elevated levels ofglucose in the patient's blood. The three most identifiable types ofdiagnosed diabetes include: pre-diabetes, type 1 diabetes, and type 2diabetes. Pre-diabetes is a condition in which blood sugar is high, butnot high enough to be type 2 diabetes. Type 2 diabetes is a chroniccondition that affects the way the body processes blood sugar. Lastly,type 1 diabetes is a chronic condition in which the pancreas produceslittle or no insulin.

Diabetes generally is diagnosed in several ways. Diagnosing diabetes mayrequire repeated testing on multiple days to confirm the positivediagnosis of a types of diabetes. Some health parameters that doctors orother suitable healthcare providers use when confirming a diabetesdiagnosis include glycated hemoglobin (A1C) levels in the blood, fastingplasma glucose (FPG) levels, oral glucose tolerance tests, and/or randomplasma glucose tests. Commonly, a healthcare provider is interested in apatient's A1C level to assist in the diagnosis of diabetes. Glycatedhemoglobin is a form of hemoglobin that is measured primarily toidentify the three-month average plasma glucose concentration that maybe used by doctors and/or other suitable healthcare providers includeweight, age, nutritional intake, exercise activity, cholesterol levels,triglyceride levels, obesity, tobacco use, and family history.

Once a diagnosis of a type of diabetes is confirmed by a doctor or othersuitable healthcare provider, the patient may undergo treatment tomanage their diabetes. Patients having their diabetes tracked ormonitored by a doctor or other healthcare provider may be treated by acombination of controlling their blood sugar through diet, exercise,oral medications, and/or insulin treatment. Regular screening forcomplications is also required for some patients. Depending on how longa patient has been diagnosed with diabetes, mHealth application 1 maysuggest a specific treatment plan to manage their condition(s). Oralmedications typically include pills taken by mouth to decrease theproduction of glucose by the liver and make muscle more sensitive toinsulin. In other instances, where the diabetes is more severe,additional medication may be required for treating the patient'sdiabetes, including injections. An injection of basal insulin, alsoknown as background insulin, may be used by healthcare providers to keepblood glucose levels at consistent levels during periods of fasting.When fasting, the patient's body steadily releases glucose into theblood to supply the cells with energy. An injection of basal insulin istherefore needed to keep blood glucose levels under control, and toallow the cells to take in glucose for energy. Basal insulin is usuallytaken once or twice a day depending on the type of insulin. Basalinsulin acts over a relatively long period of time and therefore isconsidered long acting insulin or intermediate insulin. In contrast, abolus insulin may be used to act quickly. For example, a bolus ofinsulin that may be specifically taken at meal times to keep bloodglucose levels under control following a meal. In some instances, when adoctor or healthcare provider generates a treatment plan to manage apatient's diabetes, the doctor creates a basal-bolus dose regimeninvolving, e.g., taking a number of injections throughout the day. Abasal-bolus regimen, which may include an injection at each meal,attempts to roughly emulate how a non-diabetic person's body deliversinsulin. A basal-bolus regimen may be applicable to people with type 1and type 2 diabetes. In addition to the basal-bolus regimen requiringinjections of insulin, the treatment plan may be augmented with the useof prescribed oral medications. A patient's adherence to a treatmentplan may be important in managing the disease state of the patient. Ininstances where the patient has been diagnosed with diabetes for morethan six months, for example, a very specific treatment regimen must befollowed by the patient to achieve healthy, or favorable, levels ofblood glucose. Ultimately, weekly patterns of these medication types oftreatments may be important in managing diabetes. mHealth application 1may recommend treatment plans to help patients manage their diabetes.

Exemplary Methods

As applied herein, a digital therapeutic is an evidence-basedtherapeutic intervention implemented using one or more software programsto prevent, manage, or treat a medical disorder or disease. As anexample, the mHealth application 1 of FIG. 1 is a digital therapeuticintervention. A digital therapeutic may rely on implementing behavioraland/or lifestyle changes which may be prompted by one or more collectionof digital impetuses. As a result of the digital nature of digitaltherapeutics, data can be collected and/or analyzed for improving thedigital therapeutic, improving propagation of the digital therapeutic,improving or implementing reporting based on the use of the digitaltherapeutic, or improving patient care.

Techniques disclosed herein may be used to improve a current digitaltherapeutic implementation state given that digital therapeutics arepart of a new industry with noisy or conflicting information, havinguncertain implementation models, unsettled nomenclature, a lack ofmeasurements or metrics, unique implementations, a lack of stability,and/or a lack of points of comparison. Implementations may be used tostandardized nomenclature, process configurations, apply configurationstability, broad applicability (e.g., multi disease/domain), and allowsmeasurements and benchmarking.

A digital therapeutic, such as the mHealth application 1, may beimplemented as a treatment or therapy that utilizes digital data,sensors, user data, user background information, disease data, medicalinformation, or the like to encourage or cause a change in a user'sactions, behavior, and/or habits. According to an implementation, adigital therapeutic may itself provide a treatment via an electronicdevice 19. For example, a digital therapeutic application (e.g., mHealthapplication 1) may output a digital treatment using an electronic device19. The digital treatment may be an auditory treatment, visualtreatment, olfactory treatment, haptic treatment, or a combinationthereof or the like. The digital treatment may be output using one ormore components of the electronic device 19 such as, but not limited toa speaker, a screen, a projector, a olfactory component, a hapticcomponent, or any other applicable component that is part of or may beconnected to (e.g., wired connect, wireless connection, etc.) anelectronic device 19 associated with the digital therapeutic.

A digital therapeutic may be used as a preventive measure for patientswho are at risk of developing and/or worsening medical conditions. Forexample, a user with prediabetes may be prescribed a digital therapeuticto change the user's diet and behavior that may otherwise lead to adiabetes diagnosis. A digital therapeutic can also be used as atreatment option for existing conditions. For example, a patient withtype II diabetes may use a digital therapeutic to manage the diseasemore effectively based on a treatment plan implemented using the digitaltherapeutic (e.g., mHealth application 1), as disclosed herein. Thedigital therapeutic may be used to alert, guide, or encourage the userregarding medication, exercise, diet, and/or one or more other aspectsof disease management.

According to an implementation of the disclosed subject matter, adigital therapeutic may be implemented using an electronic device 19and/or server 29 that may obtain initial data from a user 8 or about auser before generating a digital treatment plan. The user 8 may enterthe data into the electronic device 19, which may be sent to server 29.In some examples, server 29 may receive data that is relevant to ahealthcare provider, e.g., a doctor may enter related patient healthcareinformation into server 29. This data may be electronically transmittedby the provider and/or the user 8 and received by the server 29. Thedata may be electronically transmitted and received by the server 29 inany suitable manner. For example, the provider may access a digitaltherapeutic application (e.g., mHealth application 1) or secure serverand send or drop electronic data files via a network so that the filesmay be accessed by the digital therapeutic application. In someexamples, the provider may allow the digital therapeutic applicationlimited access, in compliance with any healthcare privacy regulationsand other applicable regulations, to any electronic medical records,user prescription records, referral records, etc. In some examples, theservice may electronically retrieve healthcare data from such electronicrecords (e.g., automatically). In other examples, user data may beelectronically transmitted by a user 8 and may be electronicallyreceived by the service in any suitable manner. The user data may bemanually input by the user 8 via the digital therapeutic applicationand/or may be automatically retrieved by the service from an electronicdevice of the user (e.g., electronic device 19) that may measure userhealth values, such as heart rate, blood glucose, blood oxygen, bloodpressure, activity, stress, mood, and/or sleep either periodically orcontinuously. In some examples, the user 8 may be required to complete aquestionnaire and/or survey. The questionnaire may be presented to theuser 8 during a setup of the digital therapeutic application.

In another example, initial data may be received from other applicationsoftware downloaded to a device 19 (e.g., an application on a smartphone corresponding to a fitness band tracker used to collect data oncalories burned, steps walked, or the like, by the user 8). This initialdata may be collected from the other application software eitherperiodically or continuously over a period of time.

The data received by the server may be stored in a database (e.g.,database 302 of FIG. 3). The data may be accessed at any time and may bedisplayed, printed, or updated in any suitable manner. The stored datamay be organized and accessed in any suitable manner. In some examples,the data may be electronically tagged with various identifiers, such asage, gender, clinical condition, etc.

The initial data may include an identification of one or more diseasestates of the user 8 and/or other parameters associated with the healthof the user 8. For example, initial data may include a diagnosis ofdiabetes selected from the following types: type 1, type 2,pre-diabetes, gestational diabetes, early on-set diabetes, late adulton-set diabetes, etc. Initial data may include blood glucose level,hemoglobin A1C level, blood pressure value, low-density lipid level,high-density lipid level, triglyceride cholesterol level, totalcholesterol level, body mass index (BMI), age, weight, tobacco use,alcohol use, exercise activity (e.g., step count, calories burned, heartrate), and stage of diabetes/severity of disease. Other types of dataalso may be included. In some examples, the initial data may include alength of time that the user 8 has been diagnosed with a disease, suchas, e.g., diabetes. The initial data also may include other relevantdata of the user 8, including a clinical profile of the user 8 such ashistory of diseases, significant medical events (e.g., heart attack,stroke, head trauma, transplant), lab values, user self-reportedclinical, behavioral and psycho-social data, user's demographics,medical history. In one example, when the disease is diabetes, relevantdata of the user may be clinical data of the user since the originaldiagnosis, and also may include clinical data of the user from beforethe original diagnosis. In this example, if the user 8 has had type-2diabetes for the 20 years, the relevant historical data may include atleast the user's A1C levels and/or blood glucose levels during those 20years. Other suitable data sets that may be collected include metabolicdata (e.g., blood pressure, blood glucose, weight, LDL, lab results, andthe like), medication (e.g., dose, frequency, class of medication),symptoms (e.g., structured and unstructured inputs), diet (e.g., food,calories, protein, fat, carbohydrates, sodium, allergies, and the like),activity (e.g., type, duration, intensity, and the like), andpsycho-social (e.g., financial, claims, beliefs, barriers, and thelike).

For any data collected from the user 8, metadata may be extracted fromthe stored data. In some other examples, the system, the device, and/orthe server may suggest places to eat based on geo-tagging results of theuser 8 (e.g., provide the user 8 with recommendations for restaurants inproximity to the user 8 with healthy menu options). In some examples,based on geo-tagged restaurants, restaurant menu data can be extracted,and healthier menu options from the menu data can be presented to theuser 8 (e.g., menu items containing low sugar or no sugar can bepresented to the user). In some examples, restaurant meal data may beentered into server 29 and/or device 19 by the user 8. In both examples,meal options may be presented to the user 8 based on the restaurant mealdata based on, for example, time of day, information on the user'seating habits, user personal preferences, medications, and/or exercise.

Initial data also may include medications the user 8 is currently taking(e.g., oral medications, basal injections and/or bolus injections) anddata related to the user's health and lifestyle, such as, e.g.,adherence history to prescribed medication (e.g., glycemic, oralinsulin, etc.), adherence history to prescribed medication dosage (e.g.,glycemic, oral insulin, etc.) correlated to its effect on said bloodglucose level, carbohydrate intake, weight, psycho-social determinants,and blood glucose level testing frequency correlated to its effect onsaid blood glucose level. In some examples, a user's history ofengagement frequency with the electronic device 19 also may be used inthe initial user data. For example, the digital therapeutic applicationmay generate a more complex digital treatment plan if the user 8 shows ahigh engagement frequency with electronic device 19. The initial dataalso may include data input by a healthcare provider, and may includesubjective opinions of the user 8 by the healthcare provider. Forexample, the data may include the healthcare provider's subjectiveopinions regarding the user's motivation, compliance, overall health,and the like. The digital therapeutic application may weigh thesubjective opinions of the provider when creating a treatment plan. Forexample, if the provider's subjective opinion of the user 8 is that theuser 8 has a high compliance to medication and dietary regimens, but alow compliance to exercise regimens, then a subsequent treatment plangenerated by mHealth application 1 may include a larger emphasis onmedication and diet, as opposed to exercise. Additionally, mHealthapplication 1 also may use this information to focus tutorials andeducational content sent to the user 8 on exercise topics and thebenefits of exercise relative to the user's health.

The server 29 may associate the user 8 with a cohort of other users(e.g., a group of users having similar physical, medical,psycho-determinant conditions) based on similarities between the user 8and other users. For example, a male having a height of 70 inches,weighing 190 pounds, and having high blood pressure, Indian ethnicity,type-2 diabetes, and an A1C level of approximately 6.8%, may beassociated with users of a cohort having similar characteristics. Aspreviously disclosed, a user 8 with an A1C level of greater than 6.5% isconsidered diabetic. A cohort, in this example, could be a group ofIndian American males having similar blood pressures, heights, weights,and A1C levels that have responded well to a particular treatment planand/or responded poorly to other plans. For example, a group of males ofIndian ethnicity, 68-72 inches tall, weighing 175-200 pounds, mayrespond well to an oral medication treatment for type-1 diabetes if themedication is taken twice daily at a particular dosage and timeschedule. As set forth below, goals and/or treatment plans may beassigned to the user 8 based on the association with the cohort based onresults or goals/treatments of the users within the cohort. A doctor orother suitable healthcare provider, or the digital therapeuticapplication itself, may establish a goal and/or treatment plan based ongoals and/or treatment plans that were successful within the cohort totreat a specific medical condition. In some examples, a cohort mayinclude a small number of users, e.g., two users, while in otherexamples, a cohort may include more users, e.g., dozens, hundreds, orthousands. Depending on the specific medical condition or chronicdisease, the doctor or other suitable healthcare provider wishes toaddress, the cohort may change.

The digital therapeutic application may receive one or more goals fromthe user 8 or other suitable healthcare provider, or mHealth application1 may generate a goal based on the initial data received. In otherexamples, the goal may be a default goal, such as, e.g., lowering bloodglucose levels of the user 8 when the application is a blood glucosemanagement application. The goal may include improving one or morehealth parameters of the user, such as, e.g., blood glucose level,hemoglobin A1C level, blood pressure, low-density lipid level,high-density lipid level, triglyceride cholesterol level, totalcholesterol level, body mass index (BMI), weight, user activity level,sleep duration, sleep quality, adherence to prescribed medication,nutrition (e.g., carbohydrate intake), psycho-social determinants, andblood glucose level testing frequency correlated to its effect on saidblood glucose level, among others. The goal may be determined by mHealthapplication 1 based on the previously entered information, includinginformation based on the user's disease state, history of user'sdisease, and/or other initial user data. The goal also may be determinedbased on the cohort associated with the user 8. One or more machinelearning algorithms may be used by the server 29 to help determine thegoal. In some examples, goal may include a time period after which thegoal should be achieved. For example, the goal may be to lower a user'sA1C level by a certain amount over a fixed time period (e.g., atreatment window to alleviate a specific health parameter of the user8). In some examples, the fixed time period may be one day, one week,one year, or any other suitable time period. In some examples, the timeperiod may be expanded or reduced by mHealth application 1 based on theuser's progress or compliance over the course of the time period. Forexample, mHealth application 1 may reduce a fixed treatment window from20 weeks to 16 weeks if the user's A1C levels are responding to aspecific treatment earlier than anticipated.

The goal also may include multiple parameters that should be improvedover the course of the treatment window. For example, a user 8 may set agoal of losing 10 pounds and to drop her A1C level from 6.7% to 6.3%over a 12 week time period. In another example, the digital therapeuticapplication may set a goal to reduce the user's total cholesterol levelover a 20 week time period, in addition to reducing their blood pressureto a normal level, e.g., 120/80 mm Hg from an elevated level. In thisexample, the digital therapeutic application may expand the 20 weekwindow if the user is not on track to meet their goal over the original20 week window. For example, at week 15, the digital therapeuticapplication may increase the time period for the user 8 to reach theirgoal to 30 weeks if the health parameter is determined to beunattainable within the original time period.

According to implementations, a digital therapeutics experience chain isdisclosed for optimizing the adoption of digital therapeutic solutionsin one or more populations and/or patient cohorts. FIG. 4 is a flowdiagram of an exemplary method 400 for implementing a digitaltherapeutics experience chain. In some examples, the depicted method 400may be used to implement adoption of a digital therapeutic by a patientpopulation. The method 400 provides, but is not limited to, a set ofmacro-level processes that characterize the enterprise and end-userconsumption experience with a digital therapeutic. Method 400 provides aframework to establish a common vernacular, measures to characterize adigital therapeutic implementation experience, benchmark andquantification of performance at one or more stages, and the like. Allor parts of the method 400 may be configured to optimize a portion ofthe digital therapeutic implementation experience via one or moreenvironments or entities (e.g., a hospital system, health insurer,self-insured individual, employer, etc.). As disclosed herein, outputsassociated with the one or more stages of method 400 may be used toimprove the digital therapeutic implementation such as by comparingexperience performance within similar or different environments orentities.

As shown in FIG. 4, at stage 402, one or more targets may be identified.The one or more targets may be individuals, groups, or entities that arecandidates for a given digital therapeutic solution or generally todigital therapeutic solutions. For simplicity, this disclosure maygenerally refer to targets as individuals. The one or more targets maybe identified based on one or more of clinical factors, disease factors,technology or technography factors, social factors, and/or demographicfactors. At stage 404, one or more outreach attributes may bedetermined. The outreach attributes may be techniques, systems, devices,or implementations for reaching all or a portion of the targetsidentified at stage 402. The one or more outreach attributes may bedetermined based on one or more of a method, a modality, a frequency, atime, a level of interaction, or the like. At stage 406, one or moreactivations may be provided for activating the digital therapeutic forwhich targets are identified at stage 402. The activations may beimplemented via techniques, systems, devices, or the like for activatingthe one or more targets such that the one or more targets use thedigital therapeutic or digital therapeutics in general. The activationsmay be determined based on one or more of a modality, a data-enablementverses data entry, and/or a location, or the like. At stage 408, one ormore engagement attributes may be determined. The engagement attributesmay be associated with improving the engagement of the digitaltherapeutic by one or more targets identified at stage 402. Theengagement attributes may be determined using one or more of anin-solution verses an out-of-solution engagement, frequency, length,and/or modality of engagement, or the like. FIG. 5 shows each of thefactors and their respective components. At stage 410, a report based onany one or more of the stages 402, 404, 406, or 408 may be generated.

The digital therapeutic experience chain of method 400 provides a set ofmacro-level processes that characterize the enterprise and end-userconsumption experience with a digital therapeutic. It provides aframework to establish a common vernacular, definitions, and measuresthat can be used to characterize the experience and bencher/qualifyperformance at different points throughout the experience. All or aportion of the digital therapeutic experience chain of method 400 may beused as a configurator to optimize the experience in different operatingenvironment (e.g., hospital system, health insurer, self-insuredemployer, etc.). All or a portion of the digital therapeutic experiencechain of method 400 may be used to drive continuous improvement bycomparing experience performance with similar environments and acrossdifferent environments.

At stage 402, one or more targets may be identified based on adetermination of one or more ideal candidates for a given digitaltherapeutic (e.g., the mHealth application 1) solution or for digitaltherapeutic solutions in general. The targets may be a subset ofend-users that offer an optimal opportunity to demonstrate success withuse of the digital therapeutic. The targets may be identified based onattributes of the targets and/or based on attributes of a given digitaltherapeutic. For example, if the deployment of a given digitaltherapeutic is not implementable by a given target (e.g., if the targetdoes not have a specific electronic device to implement the digitaltherapeutic), then even if that target is likely to use the digitaltherapeutic, the target may not be an identified target. Additionally,according to an implementation, a potential target's feedback potentialmay be considered when identifying the one or more targets. For example,a potential target that provides an ability to measure primaryparameters required to determine successful deployment of the digitaltherapeutic may be identified over a potential target that does not.

The identified targets may be individuals, groups, or entities that arelikely to use a given digital therapeutic or those who may benefit fromthe use of the given digital therapeutic. For example, the identifiedtargets may be individuals who are more likely than one or more otherindividuals to use the digital therapeutic application, based oninformation obtained regarding each of the individuals. As anotherexample, the identified targets may be individuals with a medicalcondition (e.g., pre-diabetes, high blood pressure, high cholesterol,diabetes, hypertension, obesity, other heart disease, etc.) that maybenefit from the digital therapeutic (e.g., a diabetes centric digitaltherapeutic such as mHealth application 1). The targets may beidentified based on one or more factors, as shown in the target section502 of FIG. 5 including, but not limited to, clinical factors 504,disease factors 506, technology or technography factors 508, socialfactors 510, demographic factors 512 (e.g., geographic factors), and/ora market segment factors 513.

Clinical factors 504 may include, but are not limited to, users withdifferent health acuity based on key clinical markers. The markers mayinclude, for example, HbA1c for diabetes, blood pressure for hypertension, weight/height for obesity, cholesterol levels for heartdisease, patient attributes for corresponding conditions, test resultsfor corresponding conditions, or the like. The clinical factors 504 mayindicate the severity of a medical condition or may be used to indicatethe potential for a user with a given medical condition to use a givendigital therapeutic. For example, a group of pre-diabetic individualsmay be identified as targets for the mHealth application 1 based on thelikelihood that the individuals can benefit from the mHealthapplication.

Clinical factors 504 may be obtained from accessing a cloud database vianetwork 32, where the database may store clinical factors 504 associatedwith a plurality of potential targets. Clinical factors 504 may be inputby the potential targets using, for example, respective electronicdevices 19. Clinical factors 504 may be correlated with a plurality ofpotential targets (e.g., while maintaining the respective potentialtarget's identity hidden). The correlation may be made using diagnosis,medical conditions, and/or electronic medical record (EMR) data andcorrelating the same with the potential targets (e.g., patient ID, EMRID, etc.). Clinical factors 504 may be obtained from health careproviders or systems (e.g., doctors, hospital networks, EMRs, etc.) vianetwork 32.

Disease factors 506 may allow targeting users with different co-morbidconditions. For example, a targeted user may be an individual who hashypertension and congestive heart failure or an individual who hasdiabetes or heart disease. Users with multiple disease factors 506 maybe more likely to use a digital therapeutic as, for example, using adigital therapeutic instead of or in conjunction with traditionalmedication may improve their individual treatment plans. As an example,a digital therapeutic may be used in conjunction with traditionalmedication to, at least in part, alert a patient regarding the timingsof medicine consumption. A user with comorbid conditions may rely on thedigital therapeutic as it may be too difficult to ensure medicinecompliance without the digital therapeutic. Clinical factors and/ordisease factors 506 may be based on metabolic conditions, medicineregimen driven, and/or co-morbidity driven.

Disease factors 506 may be obtained from accessing a cloud database vianetwork 32, where the database may store disease attributes associatedwith a plurality of potential targets. Disease factors 506 may be inputby the potential targets using, for example, respective electronicdevices 19. Disease factors 506 may be correlated with a plurality ofpotential targets (e.g., while maintaining the respective potentialtarget's identity hidden). The correlation may be made using diagnosis,medical conditions, and/or electronic medical record (EMR) data andcorrelating the same with the potential targets (e.g., patient ID, EMRID, etc.). Disease factors 506 may be obtained from health careproviders or systems (e.g., doctors, hospital networks, EMRs, etc.) vianetwork 32.

Technology or technography factors 508 may allow targeting users withdifferent levels of technological sophistication or different types ofaccess to technology. Technological sophistication may correspond to oneor more of experience with technology in general, experience with aspecific technology associated with a given digital therapeutic,proficiency with technology, comfort level using technology (e.g.,general or specific), indicated preference regarding technology, or thelike. Access to technology may include access to a network (e.g., wiredor wireless internet connection) and/or access to hardware (e.g.,connected medical devices, wearables, mobile devices, computers,laptops, tablets, IoT sensors, etc.).

Technology or technography factors 508 may be obtained based onassessing a user's technological sophistication based on observing aninteraction of a given user with a given technological device (e.g.,electronic device 19) or technological interface. The assessment may bebased on a user initiated session, based on data received from a thirdparty, or based on real-time or recorded interactions of the user.Technology or technography factors 508 may be input by the potentialtargets using, for example, respective electronic devices 19. Forexample, a user may input their comfort level using a mobile device or awearable device. Technology or technography factors 508 may bedetermined by accessing a database (e.g., via network 32) to determinethe types of devices associated with a user or user account. Forexample, a user account may consolidate information about each of theuser's devices and the user account may be accessed to determine theuser's access to technology. Technology or technography factors 508 maybe determined based on a user's purchase history. For example, a usermay purchase a medical device and that transaction may be recorded andobtained to determine that the user is able to use the given medicaldevice.

Social factors 510 may allow targeting patients with different socialdeterminants or constraints, such as levels of education, income, accessto care, mental health (e.g., anxiety, depression, distress, etc.) orthe like. Social factors 510 may determine the likelihood of a potentialtarget using a digital therapeutic. For example, a user that relies ontheir mobile device to cope with a mental health condition may be morelikely to use a digital therapeutic while a user with ample access tocare may opt to elect the care over a digital therapeutic.

Social factors 510 may be obtained by accessing a cloud database vianetwork 32, where the database may store social attributes associatedwith a plurality of potential targets. Social factors 510 may be inputby the potential targets using, for example, respective electronicdevices 19. Social factors 510 may be correlated with a plurality ofpotential targets (e.g., while maintaining the respective potentialtarget's identity hidden). The correlation may be made using electronicmedical record (EMR) data and correlating the same with the potentialtargets (e.g., patient ID, EMR ID, etc.). Social factors 510 may beobtained from health care providers or systems (e.g., doctors, hospitalnetworks, EMRs, etc.) via network 32 and/or via social networksassociated with the potential targets.

Demographic factors may allow targeting patients with differentdemographic attributes such as geography (e.g., city, state, country,region, terrain) age, rural or urban denomination, or the like.Demographic factors may determine the likelihood of a potential targetusing a digital therapeutic based on, for example, data associated withother individuals from the same or similar demographic indicatingwhether those other individuals use digital therapeutics. For example,the country of a potential target may be used to determine if thepotential target is likely to use a digital therapeutic based on use ofdigital therapeutics by other individuals form the same country.

Demographic factors may be obtained by accessing a cloud database vianetwork 32, where the database may store demographic attributesassociated with a plurality of potential targets. Demographic factorsmay be input by the potential targets using, for example, respectiveelectronic devices 19. Demographic factors may be correlated with aplurality of potential targets (e.g., while maintaining the respectivepotential target's identity hidden). The correlation may be made usingelectronic medical record (EMR) data, survey data, and/or record data,and correlating the same with the potential targets (e.g., patient ID,EMR ID, etc.). Demographic factors may be obtained from health careproviders or systems (e.g., doctors, hospital networks, EMRs, etc.) vianetwork 32 and/or social networks associated with the potential targets.

A market segment based factors 513 may allow targeting patients based onaccess to care based on different market providers. The market segmentsmay include private insurance, commercial insurance, Medicare, Medicaid,concierge coverage, or the like. Market segment factors 513 may indicatewhether a potential target user is likely to use a digital therapeutic.For example, it may be determined that users that are covered usingcommercial insurance are more likely to use digital therapeutics. Marketsegment information may be obtained by accessing a cloud database vianetwork 32, where the database may store insurance attributes associatedwith a plurality of potential targets. Market segment factors 513 may beinput by the potential targets using, for example, respective electronicdevices 19. Market segment factors 513 may be correlated with aplurality of potential targets (e.g., while maintaining the respectivepotential target's identity hidden). The correlation may be made usingelectronic medical record (EMR) data, survey data, and/or record data,and correlating the same with the potential targets (e.g., patient ID,EMR ID, etc.). Market segment factors 513 may be obtained from healthcare providers or systems (e.g., doctors, hospital networks, EMRs, etc.)via network 32.

Accordingly, at stage 402, a plurality of targets may be identifiedbased on one or more of the factors disclosed above. The plurality oftargets may be identified based on a likelihood that the identifiedplurality of targets are more likely than others to use a given digitaltherapeutic or digital therapeutics in general. The identified pluralityof targets may be stored locally or in a cloud database such as one ormore servers 29 via network 32.

At stage 404, one or more outreach attributes 514, as shown in FIG. 5,may be identified based on a determination of how best to increaseawareness of a given digital therapeutic (e.g., the mHealth application1) solution. The outreach attributes 514 may include services,methodology, individuals, groups, or entities that are likely to promoteuse of a given digital therapeutic. For example, the outreach attributes514 may identify individuals who are more often in contact with targetsthat are likely to use the given digital therapeutic. The outreachattributes 514 may be identified based on one or more factors including,but not limited to, methods 516, modalities 518, frequency 520, time522, and/or level of interaction. The outreach attributes 514 maydetermine how to most effectively reach the identified targets to use adigital therapeutic. The number of iterations of outreach (e.g., numberof campaigns, touch points, etc.) may be a factor when determiningoutreach attributes. Additionally, a scalability potential may beconsidered when determining outreach attributes 514. The scalabilitypotential may account for increased reach and/or cost-effectiveness ofoutreach.

The method 516 of outreach may be an attribute that dictates thepropagation of a given digital therapeutic. A first digital therapeuticmay be propagated faster using a first method of outreach whereas asecond digital therapeutic may be propagated faster using a secondmethod of outreach. The methods 516 of outreach may include, but are notlimited to, human outreach, automated outreach, or the like. Humanoutreach may include prescription based outreach such as from aphysician, or may include an incentive or information sharing via anindividual such as a doctor, health care professional (e.g., aregistered dietitian (RD), clinical dietary educator (CDE), socialworker (SW), etc.), user of a digital therapeutic, or the like.Automated outreach may be conducted using one or more technologicalmodalities and may be broadcast, multicast, or may be used to reach onetarget user at a time.

The modality 518 of the outreach may be in-person, via technology, viaone or more communication modes, or the like. Examples of modalities 518include, but are not limited to digital advertising, email, website,portal access, messages, social media promotions, telephone calls, chatsessions, social gatherings, in-person discussions, or the like.Physical outreach may be conducted via on premise promotion, mailpromotions, or the like.

The frequency 520 of outreach may be determined to optimize thepropagation of a given digital therapeutic. For example, a balance ratiomay be determined for the amount of outreach over a given amount of timesuch that a target user is not oversaturated by the outreach but isprovided enough touch points for the outreach to be effective. Acomprehensive outreach implementation may include varying the frequency520 of outreach per method 516 and/or per modality 518 such that a firstmethod of outreach may be more frequent than a second method ofoutreach. The frequency 520 may be determined based on an output of amachine learning model that inputs, for example, target user attributes,clinical factors 504, disease factors 506, technology factors 508,social factors 510, demographic factors 512, and/or market segmentfactors 513 to output a frequency 520. Machine learning models that maybe used are discussed further herein. The frequency 520 may be based ona user action such as a user receipt of a given promotion via a givenmodality 518 and a subsequent use or non-use of a digital therapeutic.For example, if a user sees a promotional outreach and does not usegiven digital therapeutic, then a frequency of the promotional outreachor a different promotional outreach may be adjusted based on the usernot using the given digital therapeutic.

The time 522 of outreach may be determined to optimize the propagationof a given digital therapeutic. For example, one or more optimal timesto reach a target user may be determined such that the one or moreoptimal times correspond to times when the target user is most likely touse the digital therapeutic. The time 522 of outreach may be userspecific, a target factor specific, or may be general and apply to agroup or all target users. A comprehensive outreach implementation mayinclude varying the time 522 of outreach per method 516 and/or permodality 518 such that a first method of outreach may be used at a firsttime and a second method of outreach may be used at a second time. Thetime 522 may be determined based on an output of a machine learningmodel that inputs, for example, target user attributes, clinical factors504, disease factors 506, technology factors 508, social factors 510,demographic factors 512, and/or market segment factors 513 to output atime 522. Machine learning models that may be used are discussed furtherherein. The time 522 may be based on a user action such as a userreceipt of a given promotion via a given modality 518 and a subsequentuse or non-use of a digital therapeutic. For example, if a user sees apromotional outreach at a first time and does not use given digitaltherapeutic, then a time 22 of the promotional outreach may be adjustedbased on the user not using the given digital therapeutic.

A level of interaction 524 may be determined to optimize the propagationof a given digital therapeutic. The level of interaction 524 may be acategorization of interaction such as a one-way interaction or a two-way(i.e., interactive) interaction. Alternatively, or in addition, thelevel of interaction 524 may include a duration, depth, or extent ofinteraction. For example, a human interaction may be two minutes long orfifteen minutes long. However, it may be determined that an outreachgreater than three minutes provides diminishing returns. Accordingly,the level of interaction 524 may be capped at three minutes to preventover-saturating a target user. The level of interaction 524 may bedetermined based on an output of a machine learning model that inputs,for example, target user attributes, clinical factors 504, diseasefactors 506, technology factors 508, social factors 510, demographicfactors 512, and/or market segment factors 513 to output a level ofinteraction 524. Machine learning models that may be used are discussedfurther herein.

At stage 406, one or more activations 526, of FIG. 5, may be providedbased on a determination of how best to activate a digital therapeutic(e.g., the mHealth application 1) solution for one or more target users.The activation 526 may be based on one or more of a modality 528,data-enablement verses data-entry 530, and/or location 532 and may behuman-assisted (e.g., at a clinic or office, via human communication,remote assisted, tele-assisted, etc.), technology driven (e.g., via amobile application, electronic prompts, etc.) and/or automated viatechnology (e.g., via a non-deep link, deep link, automated bot, etc.).The provided activation 526 may be selected to maximize an activation tooutreach ratio such that the provided activation 526 is most effectivegiven one or more corresponding outreach attributes 514.

The modality 518 of the activation may be assisted, self-activated, orthe like. Examples of modalities 518 include, a user self activating thedigital therapeutic, the user receiving guidance via a technologicalplatform (e.g., email, messaging, application portal, social mediaportal, guided or automated chats, etc.), telephone calls, or the like.For example, a user may receive a link at a given time 522 such that theselection of the link results in activation of the digital therapeutic.As another example, a user may receive a phone call from an automatedsystem to remind the user to activate the digital therapeutic at a giventime. According to an implementation, users may be able to select theirpreferred modality based on one or more modality options. According toanother implementation, the modality 518 may be determined based on anoutput of a machine learning model that inputs, for example, target userattributes, clinical factors 504, disease factors 506, technologyfactors 508, social factors 510, demographic factors 512, and/or marketsegment factors 513 to output a modality 518. Machine learning modelsthat may be used are discussed further herein.

Data-enablement verses data-entry 530 may refer to the pre-population ofdata verses a user entry of data. A determination may be made regardingthe lack of activation based on the amount of data a user may need toenter. Accordingly, available data may be pulled from one or moredevices and/or systems including, but not limited to, electronic devices19, EMR 14, pharmacy 9, servers 29, or the like and may be received vianetwork 32. The data enablement may be based on the target userattributes, clinical factors 504, disease factors 506, technologyfactors 508, social factors 510, demographic factors 512, market segmentfactors 513 to output a modality 518, electronic devices 19, prior userinputs, and/or the like.

Activation may be provided based on location 532 which may be anyapplicable location that a digital therapeutic can be activated.Location 532 may be a doctor's office, heath care provider location,home, work, or the like. The location 532 may be a location that changesfrom a first activation to a second activation. Further, location 532for a single activation may vary such as, for example, if a useractivates a given digital therapeutic while traveling or commuting.

At 408, one or more engagement attributes may be determined. Theengagement attributes may be determined to maximize engagement with thedigital therapeutic or based on the digital therapeutic. Engagementbased on the digital therapeutic may be different than engagement withthe digital therapeutic such that engagement based on the digitaltherapeutic may utilize tools, processes, techniques, etc. outside anapplication associated with the digital therapeutic. For example, adigital therapeutic application may determine an optimal time for a userto take glucose medication. However, instead of or in addition toalerting a user to take the glucose medication at the optimal time, thedigital therapeutic may cause an SMS message to be sent to the user'sphone. Accordingly, the engagement with the SMS message may be based onthe digital therapeutic. As shown in FIG. 5, the engagement attributes534 may be based on in-solution (e.g., within a digital therapeuticapplication) verses out-of-solution 536. The one or more engagementattributes may be based on identifying which subsets of users exhibitcommon engagement patterns and further based on what defines thesepatterns (e.g., demographics, drug regimens, use of specificin-application features, etc.). The engagement attributes may be basedutilizing health care professionals to optimize engagement by users witha given digital therapeutic (e.g., in a manner that is easy to use,manageable, fits into workflow, etc.). Engagement attributes may defineor may be selected based on engagement metrics that provide feedbackregarding a given engagement.

Engagement attributes 534 may further be based on a frequency 538 ofengagement, length 540 of engagement, and/or modality 542 of engagement.As examples, engagement be via a software application related to thedigital therapeutic (e.g., guided data collection, real-time feedback(RTFB), longitudinal feedback (LFB), insights, education, support,comprehensive data analysis (CDA), Artificial Intelligence, etc.), via aprescribing healthcare provider (e.g., that may order use of the digitaltherapeutic, review clinical decision support or clinical decisionsystem (CDS), adjust the digital therapeutic plan, etc.), via a careteam or program administrator (e.g., individual intervention, populationintervention, etc.), via customer care (e.g., onboarding, humanengagement, trouble management, prescription upgrades, etc.), via anartificial intelligence engine (e.g., human analytics and intervention,automated analytics and intervention, etc.), or the like.

A frequency 538 of engagement may be the amount of times a digitaltherapeutic is engaged in a given amount of time. The frequency 538 maybe optimized to balance a necessity (e.g., medical compliance, exercisecompliance, dietary compliance, etc.) with engagement saturation. Thefrequency 538 may be individual based or may be based on a group or allusers. A length 540 of engagement may be the duration of an engagementand/or the duration of an activity, or task conducted based on anengagement. The length 540 may be optimized to promote use of thedigital therapeutic as necessitated by a given user's conditions orneeds. The modality 542 of engagement or treatment may be any applicablemedium such as an application, webpage, software, wearable device,mobile device, holographic device, telephone, virtual reality (VR)system, augmented reality system (AR), or the like.

At stage 410, a report may be generated. The report may be based on anydata related to stages 402, 404, 406, and 408 of method 400 of FIG. 4,as disclosed herein. The report may include one or more components ofenrollment for a given digital therapeutic, outcomes from one or morestages of method 400, or the like. For example, the report may includeone or more of cost metrics, effectiveness metrics, time metrics,individual cost metrics, population based cost metrics, individualeffectiveness metrics, population based effectiveness metrics,individual time metrics, population based time metrics, or the like.According to an implementation, the report may impute a value to theintegrated approach to implementing a digital therapeutic solution viamethod 400. The report may identify where integration should occur(e.g., at one or more stages of method 400) within a digital therapeuticexperience chain (e.g., method 400) as well as one or more sources orcomponents of value that the integration contributes.

The report may be generated in any applicable format such that it usableby a human user (e.g., a screen, a webpage, application, PDF, excel,text based report, or the like) or by an automated system (e.g., afeedback loop, a machine learning model, etc.). The report may provideone or more of an informative analysis (e.g., based on informationreceived from a patient or health care system), discovery analysis(e.g., based on items discovered during implementation of method 400),extrapolative analysis (e.g., identification of potential changes),adaptive analysis (e.g., results from implementing one or more changes),or the like. The report may be provided such that it is organized,searchable, and/or filterable. For example, the report may be filterableto by one or more of the factors or attributes shown in FIG. 5 (e.g.,clinical factors 504, method of outreach 514, modality 528 ofactivation, frequency 538 of engagement, etc.).

The report may be generated to identify the most useful data to improveor validate a digital therapeutic experience chain, as shown via thestages in method 400. For example, for a first stage or subsequentstage, the report may show relevant data associated with that stageand/or data that would allow improvement of that stage. For example, thereport may include data used to identify targets at stage 402 and mayuse the activation data from stage 406 to provide insight about thetargets at stage 402. The report may be generated by using the availabledata to discover trends, patterns, and/or insights. The report mayidentify practices and/or policies to be put into place to meet gooddata science governance policies.

FIG. 6 shows a method 600 for outputting comparison results from two ormore stages of method 400 and applying the comparison results to amachine learning model. As disclosed herein, the comparison results maybe include in the report generated at stage 410 of method 400.

At step 602, an N stage score may be received. An N stage may correspondto any one of the stages 402, 404, 406, 408, or 410 of method 400. An Nstage score may correspond to a score associated with implementation ofthe respective stage. The N stage score may be based on the success ofthat given stage as determined on stage specific factors. The success ofa given stage may be based on predetermined or dynamically determinedcriteria or may be based on comparison to previous iterations of a givenstage. As examples, stage 402 may include target identification factors(e.g., number of targets identified, ratio of targets identified,comparison of targets identified with previously identified targets,etc.), stage 404 may include outreach factors (e.g., click-throughrates, view rates, user involvement rates, percentage of targetsreached, etc.), stage 406 may include activation based factors (e.g.,frequency of activation, percentage of users that activated the digitaltherapeutic in a given amount of time, cause of activation, consistencyof activation, etc.), stage 408 may include engagement based factors(e.g., duration of engagement, quality of engagement, frequency ofengagement, result from engagement, etc.). At 604 an N+1 stage score maybe received. The N+1 stage may correspond to a stage subsequent to the Nstage at step 602. For example, if the N stage score is from stage 402,then the N+1 stage score corresponds to a score for stage 404.

At 606, the N+1 stage score may be compared to the N stage score. Thecomparison may be any applicable comparison such as a ratio between theN stage score and the N+1 stage score, a difference between the N stagescore and the N+1 stage score, a change in the N stage score vs a changein the N+1 stage score or the like. The comparison may be a number, aranking, a designation, a fraction, or the like. The comparison of theN+1 stage score to the N stage score may provide insight into one orboth of the stages. For example, if the N stage score isdisproportionately higher than the N+1 stage score (e.g., as indicatedby a ratio), then the difference may trigger an adjustment to the N+1stage. As a specific example if, at stage 402, the score associated withidentifying targets is 100 but the outreach, at stage 404, to thosetargets is 20, this may indicate a large discrepancy between stage 402and 404. Accordingly, as a result, one or more changes may be triggered.For example, during a subsequent iteration, more resources may beexpended for the outreach stage 404 in comparison to the targetidentification stage 402.

At 608, the comparison results may be output. The output may be part ofa report, generated at stage 410 of method 400 or may be outputindependently of a report. The comparison results may be output via anyapplicable format (e.g., a screen, a webpage, application, PDF, excel,text based report, or the like). The comparison result may be outputinto an input component of a digital therapeutic system such that achange to one or more of the stages of method 400 is implemented basedon the comparison result.

Alternatively, or additionally, at 610, a comparison result may beapplied to a machine learning model. The comparison result may be theinput or may be one of the inputs to the machine learning model suchthat a change to one or more of the stages of method 400 is implementedbased on the comparison result based machine learning model output. FIG.7 shows a comparison result 706 component, as further disclosed herein.

One or more of the stages of method 400, factors or attributes of FIG.5, and/or comparisons of method 600 may be implemented based on theoutput of one or more machine learning models. For simplicity, a singlemachine learning model will be discussed herein but it will beunderstood that multiple machine learning models may be used forrespective different outputs. A machine learning model may be trainedusing a dataset including supervised, partially supervised, orunsupervised sample digital therapeutic data (e.g., from actual orsimulated stages of method 400). For example, a learning algorithm ornetwork (e.g., clustering algorithm, a neural network, a deep learningnetwork, a genetic learning algorithm, or algorithms based onConvolutional Neural Networks (CNN), CNN with multiple-instance learningor multi-label multiple instance learning, Recurrent Neural Networks(RNN), Long-short term memory RNN (LSTM), Gated Recurrent Unit RNN(GRU), graph convolution networks, etc.) may be provided digitaltherapeutic data. By applying a large plurality of such digitaltherapeutic data, the machine learning algorithm may be used to train amachine learning model provides applicable outputs (e.g., targets,outreach attributes, activations, engagement attributes, reports, one ormore factors or attributes from FIG. 5, etc.).

FIG. 7 shows an example training module 700 to train a digitaltherapeutic system machine learning model. As shown in FIG. 7, trainingdata 702 may include one or more of stage inputs 704 (e.g., one or moreoutputs from a stage from method 400, a factor or attribute from FIG. 5,etc.), and known outcomes 708 (i.e., known or desired outputs for futureinputs similar to or in the same category as stage inputs 704 that donot have corresponding known outputs) related to a digital therapeuticsystem. The training data 702 and a training algorithm 710 may beprovided to a training component 720 that may apply the training data702 to the training algorithm 710 in order to generate a digitaltherapeutic machine learning model. According to an implementation, thetraining component 720 may be provided comparison results from step 610of method 600. The comparison result may be used by the trainingcomponent 720 to update the digital therapeutic machine learning model.For example, a ratio of stage 404 compared to stage 402 may be used tomodify the digital therapeutic machine learning model such that itfavors outreach over target identification for a subsequent iteration ofthe method 400.

FIG. 8 is a simplified functional block diagram of a computer that maybe configured as a host server, for example, to function as healthcareprovider decision-making server. FIG. 8 illustrates a network or hostcomputer platform 800. It is believed that those skilled in the art arefamiliar with the structure, programming, and general operation of suchcomputer equipment and as a result, the drawings should beself-explanatory.

A platform for a server 800 or the like, for example, may include a datacommunication interface for packet data communication 860. The platformalso may include a central processing unit (CPU) 820, in the form of oneor more processors, for executing program instructions. The platformtypically includes an internal communication bus 810, program storage,and data storage for various data files to be processed and/orcommunicated by the platform such as ROM 830 and RAM 840 or the like.The hardware elements, operating systems, and programming languages ofsuch equipment are conventional in nature, and it is presumed that thoseskilled in the art are adequately familiar therewith. The server 800also may include input and output ports 850 to connect with input andoutput devices such as keyboards, mice, touchscreens, monitors,displays, etc., and communication ports 860. Of course, the variousserver functions may be implemented in a distributed fashion on a numberof similar platforms to distribute the processing load. Alternatively,the servers may be implemented by appropriate programming of onecomputer hardware platform.

The report or any applicable output may further identify the relativevalue that one or more points of integration provide. For example,potential points of integration may include program integration (e.g.,providing an organized structure around implementation of a digitaltherapeutic solution), health care provider integration (e.g., engaginga clinical key entity at various points in a digital therapeuticexperience chain), system or EMR integration (e.g., reducing friction inease of provider interactions as well as data accessibility with thedigital therapeutic).

Experimental results have shown that an incremental effect on keydigital therapeutic metrics sources of value, as provided in chart 900of FIG. 9. As shown in legend 902, persistence is indicated by a dotpattern, engagement rate is indicated by a check pattern, and anactivation rate is indicated by a solid shape. As shown, in comparisonto a baseline 904 of having a direct to consumer implementation (i.e.,no digital therapeutic experience chain such as method 400), a programthat includes involvement by an employer or payer at 906 shows improvedtraining effectiveness, increased access and efficiency, access tosupport, and increased touchpoints. In this scenario, persistenceincreased by 4×, engagement by 2×, and activation rate by 15×. As shown,in comparison to a baseline 904 of having a direct to consumerimplementation with an incremental program that includes involvement byan employer or payer at 906, a program that involved a healthcareprovider (e.g., health system, payer) shows improved access to analyzedpatient-generated health data (PGHD), improved outcomes through therapyoptimization, improved quality measures (e.g., Centers for Medicare &Medicate Services (CMS)Health Effectiveness Data and Information Set(HEIS), CMS Star Rating, etc.). In this scenario, persistence increasedby 5×, engagement by 4×, and activation rate by 26×. As shown, incomparison to a baseline 904 with an incremental program that includesinvolvement by an employer or payer at 906, and an incremental programthat involved a healthcare provider (e.g., health system, payer) at 908,a program that ads integration (e.g., EMR integration) shows one touchactivation (e.g., reduce activation friction), ease of order, monitoringof digital therapeutic tools, ease of integration into EMR drivenworkflow, rich data for population health insights. In this scenario,persistence increased by 7.5×, engagement by 7×, and activation rate by39×.

Program aspects of the technology may be thought of as “products” or“articles of manufacture” typically in the form of executable codeand/or associated data that is carried on or embodied in a type ofmachine-readable medium. “Storage” type media include any or all of thetangible memory of the computers, processors or the like, or associatedmodules thereof, such as various semiconductor memories, tape drives,disk drives and the like, which may provide non-transitory storage atany time for the software programming. All or portions of the softwaremay at times be communicated through the Internet or various othertelecommunication networks. Such communications, for example, may enableloading of the software from one computer or processor into another, forexample, from a management server or host computer of the mobilecommunication network into the computer platform of a server and/or froma server to the mobile device. Thus, another type of media that may bearthe software elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links, or the like, also may be considered as mediabearing the software. As used herein, unless restricted tonon-transitory, tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

It would be apparent to one of skill in the relevant art that thepresent disclosure, as described herein, can be implemented in manydifferent examples of software, hardware, firmware, and/or the entitiesillustrated in the figures. Any actual software code with thespecialized control of hardware to implement examples is not limiting ofthe detailed description. Thus, the operational behavior of exampleswill be described with the understanding that modifications andvariations of the examples are possible, given the level of detailpresented herein.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed examples, as claimed.

Other examples of the disclosure will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

What is claimed is:
 1. A computer-implemented method for deploying adigital therapeutic program, the method comprising: determining alikelihood of use of the digital therapeutic for each of a plurality ofusers, wherein a machine learning model receives a plurality of usercharacteristics to output the likelihood of use of the digitaltherapeutic for each of the plurality of users, wherein each of theplurality of users can access the digital therapeutic using a singlerespective account linked to one or more respective target userelectronic devices, wherein the digital therapeutic for a given userfrom the plurality of users communicates with at least one externalapplication associated with the given user, each respective target userelectronic device configured to received data from a clinical dataserver and a user interface server, the user interface server configuredto receive and process user inputs, each respective target userelectronic device comprising a presentation layer selected from a webbrowser, application, or messaging interface, wherein the digitaltherapeutic is configured to output a treatment based on: initial datacomprising current medication, adherence history to prescribedmedications, carbohydrate intake, weight, and blood glucose levels; auser history of engagement with a respective target user electronicdevice; and an identified cohort of users associated with each of theplurality of users, respectively, the identified cohort of usersdetermined based on one of a similarity in physical condition,similarity in medical condition, or similarity in psycho determinantcondition; determining a feedback potential for each of the plurality ofusers, the feedback potential corresponding to a likelihood of feedbackof the digital therapeutic, wherein the machine learning model outputsthe feedback potential; identifying a plurality of target users from theplurality of users for the digital therapeutic based on one or moretarget parameters comprising the likelihood of use of the digitaltherapeutic and the feedback potential for each of the plurality oftarget users; determining access to technology for each of the pluralityof users; determining a technological sophistication for each of theplurality of users; determining a market segment factor for each of theplurality of users, the market segment factor comprising a likelihood ofinsurance coverage for each respective plurality of users; identifyingthe plurality of target users further based on the access to technologyfor each of the plurality of users, and excluding the plurality of usersfor whom the access to technology is not sufficient; identifying theplurality of users further based on the technological sophistication foreach of the plurality of users, and excluding the plurality of users forwhom the technological sophistication is not sufficient; identifying theplurality of users further based on the market segment factor andexcluding the plurality of users for whom the market segment factorindicates a low likelihood of insurance coverage; identifying optimaloutreach for one or more of the plurality of target users using anautomated outreach medium, wherein: identifying an activation tooptimize use of the digital therapeutic using respective target userelectronic devices wherein the machine learning model identifies theactivation based the user characteristics, past activation, and pastcharacteristics; providing medical treatments via the digitaltherapeutic on respective target user electronic devices, based onimporting activity tracking device data for the respective target users,the medical treatments comprising a specific treatment plan; receivingan engagement level of the digital therapeutic by one or more of theplurality of target users; and updating the machine learning model basedon the received engagement level and further by a training componentbased on a comparison result of the plurality of target users to anumber of user reached via the optimal outreach.
 2. Thecomputer-implemented method of claim 1, further comprising generating areport based on one or more of the target users, the outreach, theactivation, or activating the digital therapeutic.
 3. Thecomputer-implemented method of claim 2, wherein the report is based onone or more of an informative analysis, discovery analysis,extrapolative analysis, or an adaptive analysis.
 4. Thecomputer-implemented method of claim 2, wherein the report comprises acomparison of an N+1 stage score to an N stage score for each stageexcept a final stage.
 5. The computer-implemented method of claim 1,wherein the activation is based on one or more of a modality,data-enablement verses data-entry, or a location.
 6. Thecomputer-implemented method of claim 1, wherein the engagement level isbased on one or more of an in-solution versus out-of-solution, afrequency, a length, or a modality, wherein the in-solution correspondsto the digital therapeutic and the out-of-solution is external to thedigital therapeutic.
 7. The computer-implemented method of claim 1,wherein at least one of the identifying the plurality of target users,conducting outreach, identifying an activation, and activating theactivation is based on an output of a machine learning model.
 8. Thecomputer-implemented method of claim 7, wherein the machine learningmodel is trained using training data that comprises one or more of stageinputs, known outcomes, and comparison results.
 9. Thecomputer-implemented method of claim 8, wherein the comparison resultsare a ratio of an N+1 stage score to an N stage score for each stageexcept a final stage.
 10. A system for deploying a digital therapeutic,the system comprising: a data storage device storing a machine learningmodel, wherein the machine learning model is trained using at least oneof supervised training or unsupervised training; and a processoroperatively connected to the data storage device and configured toexecute the machine learning model for: determining a likelihood of useof the digital therapeutic for each of a plurality of users, wherein amachine learning model receives a plurality of user characteristics tooutput the likelihood of use of the digital therapeutic for each of theplurality of users, wherein each of the plurality of users can accessthe digital therapeutic using a single respective account linked to oneor more respective target user electronic devices, wherein the digitaltherapeutic for a given user from the plurality of users communicateswith at least one external application associated with the given user,each respective target user electronic device configured to receiveddata from a clinical data server and a user interface server, the userinterface server configured to receive and process user inputs, eachrespective target user electronic device comprising a presentation layerselected from a web browser, application, or messaging interface,wherein the digital therapeutic is configured to output a treatmentbased on: initial data comprising current medication, adherence historyto prescribed medications, carbohydrate intake, weight, and bloodglucose levels; a user history of engagement with a respective targetuser electronic device; and an identified cohort of users associatedwith each of the plurality of users, respectively, the identified cohortof users determined based on one of a similarity in physical condition,similarity in medical condition, or similarity in psycho determinantcondition; determining a feedback potential for each of the plurality ofusers, the feedback potential corresponding to a likelihood of feedbackof the digital therapeutic, wherein the machine learning model outputsthe feedback potential; identifying a plurality of target users from theplurality of users for the digital therapeutic based on one or moretarget parameters comprising the likelihood of use of the digitaltherapeutic and the feedback potential for each of the plurality oftarget users; determining access to technology for each of the pluralityof users; determining a technological sophistication for each of theplurality of users; determining a market segment factor for each of theplurality of users, the market segment factor comprising a likelihood ofinsurance coverage for each respective plurality of users; identifyingthe plurality of target users further based on the access to technologyfor each of the plurality of users, and excluding the plurality of usersfor whom the access to technology is not sufficient; identifying theplurality of users further based on the technological sophistication foreach of the plurality of users, and excluding the plurality of users forwhom the technological sophistication is not sufficient; identifying theplurality of users further based on the market segment factor andexcluding the plurality of users for whom the market segment factorindicates a low likelihood of insurance coverage; identifying optimaloutreach for one or more of the plurality of target users using anautomated outreach medium, wherein: identifying an activation tooptimize use of the digital therapeutic using respective target userelectronic devices wherein the machine learning model identifies theactivation based the user characteristics, past activation, and pastcharacteristics; providing medical treatments via the digitaltherapeutic on respective target user electronic devices, based onimporting activity tracking device data for the respective target users,the medical treatments comprising a specific treatment plan; receivingan engagement level of the digital therapeutic by one or more of theplurality of target users; and updating the machine learning model basedon the received engagement level and further by a training componentbased on a comparison result of the plurality of target users to anumber of user reached via the optimal outreach.
 11. The system of claim10, wherein the one or more target parameters comprises a market segmentbased factor, wherein the market segment based factor is selected fromone or more of a private insurance, a commercial insurance, Medicare,Medicaid, or a concierge coverage.
 12. The system of claim 10, furthercomprising generating a report based on one or more of cost metrics,effectiveness metrics, time metrics, individual cost metrics, populationbased cost metrics, individual effectiveness metrics, population basedeffectiveness metrics, individual time metrics, population based timemetrics, the feedback potential, the target parameters, or the outreach.13. The system of claim 10, wherein the machine learning model isfurther trained my modifying one of one or more weights or one or morelayers based on training data.
 14. A non-transitory computer-readablemedium storing instructions that, when executed by processor, cause theprocessor to perform operations for deploying a digital therapeuticprogram, the operations comprising: determining a likelihood of use ofthe digital therapeutic for each of a plurality of users, wherein amachine learning model receives a plurality of user characteristics tooutput the likelihood of use of the digital therapeutic for each of theplurality of users, wherein each of the plurality of users can accessthe digital therapeutic using a single respective account linked to oneor more respective target user electronic devices, wherein the digitaltherapeutic for a given user from the plurality of users communicateswith at least one external application associated with the given user,each respective target user electronic device configured to receiveddata from a clinical data server and a user interface server, the userinterface server configured to receive and process user inputs, eachrespective target user electronic device comprising a presentation layerselected from a web browser, application, or messaging interface,wherein the digital therapeutic is configured to output a treatmentbased on: initial data comprising current medication, adherence historyto prescribed medications, carbohydrate intake, weight, and bloodglucose levels; a user history of engagement with a respective targetuser electronic device; and an identified cohort of users associatedwith each of the plurality of users, respectively, the identified cohortof users determined based on one of a similarity in physical condition,similarity in medical condition, or similarity in psycho determinantcondition; determining a feedback potential for each of the plurality ofusers, the feedback potential corresponding to a likelihood of feedbackof the digital therapeutic, wherein the machine learning model outputsthe feedback potential; identifying a plurality of target users from theplurality of users for the digital therapeutic based on one or moretarget parameters comprising the likelihood of use of the digitaltherapeutic and the feedback potential for each of the plurality oftarget users; determining access to technology for each of the pluralityof users; determining a technological sophistication for each of theplurality of users; determining a market segment factor for each of theplurality of users, the market segment factor comprising a likelihood ofinsurance coverage for each respective plurality of users; identifyingthe plurality of target users further based on the access to technologyfor each of the plurality of users, and excluding the plurality of usersfor whom the access to technology is not sufficient; identifying theplurality of users further based on the technological sophistication foreach of the plurality of users, and excluding the plurality of users forwhom the technological sophistication is not sufficient; identifying theplurality of users further based on the market segment factor andexcluding the plurality of users for whom the market segment factorindicates a low likelihood of insurance coverage; identifying optimaloutreach for one or more of the plurality of target users using anautomated outreach medium, wherein: identifying an activation tooptimize use of the digital therapeutic using respective target userelectronic devices wherein the machine learning model identifies theactivation based the user characteristics, past activation, and pastcharacteristics; providing medical treatments via the digitaltherapeutic on respective target user electronic devices, based onimporting activity tracking device data for the respective target users,the medical treatments comprising a specific treatment plan; receivingan engagement level of the digital therapeutic by one or more of theplurality of target users; and updating the machine learning model basedon the received engagement level and further by a training componentbased on a comparison result of the plurality of target users to anumber of user reached via the optimal outreach.
 15. The system of claim10, wherein the one or more target parameter is identified based on oneor more of attributes of the target users or attributes of the digitaltherapeutic.
 16. The non-transitory computer-readable medium of claim14, further comprising generating a report based on one or more of thetarget users, the outreach, the activation, or activating the digitaltherapeutic.
 17. The non-transitory computer-readable medium of claim16, wherein the report comprises a comparison of an N+1 stage score toan N stage score for each stage except a final stage.